Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease



Abstract Background Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome. Methods We performed a prospective cohort study of 19 treatment-naïve pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients’ pretreatment samples. Results Patients with Crohn’s disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn’s disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status. Conclusions Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.
Saatavilla1 tammikuuta 2016



  • Kelly A. Shaw (Contributor)
  • Madeline Bertha (Contributor)
  • Tatyana Hofmekler (Contributor)
  • Pankaj Chopra (Contributor)
  • Tommi Vatanen (Contributor)
  • Abhiram Srivatsa (Contributor)
  • Jarod Prince (Contributor)
  • Archana Kumar (Contributor)
  • Cary Sauer (Contributor)
  • Michael E. Zwick (Contributor)
  • Glen A. Satten (Contributor)
  • Aleksandar D. Kostic (Contributor)
  • Jennifer G. Mulle (Contributor)
  • Ramnik J. Xavier (Contributor)
  • Subra Kugathasan (Contributor)


ID: 21294893